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US Patent 12078715 Radar tracking with model estimates augmented by radar detections

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
0
Patent Number
120787150
Patent Inventor Names
David Aaron Schwartz0
Susan Yu-Ping Chen0
Nianxia Cao0
Jan K. Schiffmann0
Date of Patent
September 3, 2024
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Patent Application Number
176497940
Date Filed
February 2, 2022
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Patent Citations
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US Patent 9240053 Target tracking
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US Patent 10451712 Radar data collection and labeling for machine learning
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US Patent 9983301 Automated vehicle radar system to determine yaw-rate of a target vehicle
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US Patent 11852746 Multi-sensor fusion platform for bootstrapping the training of a beam steering radar
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US Patent 6873251 Tracking system and method employing multiple overlapping sensors
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US Patent 7026979 Method and apparatus for joint kinematic and feature tracking using probabilistic argumentation
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Patent Primary Examiner
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Zhen Y Wu
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CPC Code
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G01S 2013/93271
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G01S 13/931
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G01S 13/878
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G01S 13/66
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Patent abstract

This document describes radar tracking with model estimates augmented by radar detections. An example tracker analyzes information derived using radar detections to enhance radar tracks having object measurements estimated from directly analyzing data cubes with a model (e.g., a machine-learning model). High-quality tracks with measurements to objects of importance can be quickly produced with the model. However, the model only estimates measurements for classes of objects its training or programming can recognize. To improve estimated measurements from the model, or even in some cases, to convey additional classes of objects, the tracker separately analyzes detections. Detections that consistently align to objects recognized by the model can update model-derived measurements conveyed initially in the tracks. Consistently observed detections that do not align to existing tracks may be used to establish new tracks for conveying more classes of objects than the model can recognize.

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